Advertisement

Business Rule Optimisation: Problem Definition, Proof-of-Concept and Application Areas

  • Alan DormerEmail author
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 339)

Abstract

Business rules have been applied to a wide range of manufacturing and services organisations. Decisions around quality control, customer acceptance, and warranty claims are typical applications in day-to-day operation. They all have two things in common; there are multiple assessment criteria such as profit, revenue, and customer satisfaction, and the quality of the decisions made have an impact on the performance and sustainability of the organisation. This paper presents a solution to the novel problem of optimising the structure and parameters of automated business rules where there is the possibility to refer some decisions to a human expert. The difference here is that although the business rules are deterministic and repeatable, human decisions are generally neither. This research problem is multi-disciplinary, and the solution comprises elements of business process management, mathematical optimisation, simulation, machine learning, probability, and psychology. The paper describes a potential solution, some initial results when applied to a problem in the financial services sector and identifies further areas of application.

Keywords

Business process management Business Intelligence Mathematical optimisation Machine learning Decision support 

References

  1. 1.
    Business Rules Group: Final Report, Revision 1.3, July 2000Google Scholar
  2. 2.
    Jeston, J., Nelis, J.: Business Process Management. Routledge, Abingdon (2014). ISBN 9781136172984CrossRefGoogle Scholar
  3. 3.
    Wang, O., Liberti, L.: Controlling some statistical properties of business rules programs. In: Battiti, R., Kvasov, D.E., Sergeyev, Y.D. (eds.) LION 2017. LNCS, vol. 10556, pp. 263–276. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69404-7_19CrossRefGoogle Scholar
  4. 4.
    Wang, O., Liberti, L., D’Ambrosio, C., de Sainte Marie, C., Ke, C.: Controlling the average behavior of business rules programs. In: Alferes, J.J.J., Bertossi, L., Governatori, G., Fodor, P., Roman, D. (eds.) RuleML 2016. LNCS, vol. 9718, pp. 83–96. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-42019-6_6CrossRefGoogle Scholar
  5. 5.
    Kunz, T.P., Crone, S.F.: The impact of practitioner business rules on the optimality of a static retail revenue management system. J. Revenue Pricing Manag. 14(3), 198–210 (2015)CrossRefGoogle Scholar
  6. 6.
    Quinzaños, J.M., Cartas, A., Vidales, P., Maldonado, A.: iDispatcher: using business rules to allocate and balance workloads. In: DSS, pp. 110–119, May 2014Google Scholar
  7. 7.
    Hegazi, M.O.: Measuring and predicting the impacts of business rules using fuzzy logic. Int. J. Comput. Sci. Inf. Secur. 13(12), 59 (2015)Google Scholar
  8. 8.
    Dormer, A.: Optimising business rules in the services sector. Int. J. Soc. Behav. Educ. Econ. Bus. Ind. Eng. 6(10), 2580–2584 (2012)Google Scholar
  9. 9.
    Dormer, A.: A framework for optimising business rules. In: Abramowicz, W. (ed.) BIS 2017. LNBIP, vol. 303, pp. 5–17. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-69023-0_1CrossRefGoogle Scholar
  10. 10.
    Taylor, J.: Decision Management Systems: A Practical Guide to Using Business Rules and Predictive Analytics. IBM Press, Indianapolis (2011). ISBN 0-13-288438-0Google Scholar
  11. 11.
    Turney, P.D.: Cost-sensitive classification: empirical evaluation of a hybrid genetic decision tree induction algorithm. J. Artif. Intell. Res. 2, 369–409 (1995)CrossRefGoogle Scholar
  12. 12.
    Zadrozny, B., Langford, J., Abe, N.: Cost-sensitive learning by cost-proportionate example weighting. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 435–442. IEEE, November 2003Google Scholar
  13. 13.
    Vergidis, K., Tiwari, A., Majeed, B.: Business process analysis and optimization: beyond reengineering. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 38(1), 69–82 (2008)CrossRefGoogle Scholar
  14. 14.
    Laguna, M., Marklund, J.: Business Process Modeling, Simulation and Design. CRC Press, Boca Raton (2013)Google Scholar
  15. 15.
    Rajamma, R.K., Paswan, A.K., Hossain, M.M.: Why do shoppers abandon shopping cart? Perceived waiting time, risk, and transaction inconvenience. J. Prod. Brand Manag. 18(3), 188–197 (2009)CrossRefGoogle Scholar
  16. 16.
    Chandrashekar, G., Sahin, F.: A survey on feature selection methods. Comput. Electr. Eng. 40(1), 16–28 (2014)CrossRefGoogle Scholar
  17. 17.
    Brunswik, E.: The essential Brunswik: beginnings, explications, applications, new directions in research on decision making. In: Research Conference on Subjective Probability, Utility and Decision Making, Helsinki, Finland (1985)Google Scholar
  18. 18.
    Jonsson, P., Kjellsdotter, L., Rudberg, M.: Applying advanced planning systems for supply chain planning: three case studies. Int. J. Phys. Distrib. Logist. Manag. 37(10), 816–834 (2007)CrossRefGoogle Scholar
  19. 19.
    Quinlan, J.R.: Generating production rules from decision trees. In: IJCAI, vol. 87 (1987)Google Scholar
  20. 20.
  21. 21.
    Berbeglia, G., Cordeau, J.F., Gribkovskaia, I., Laporte, G.: Static pickup and delivery problems: a classification scheme and survey. Top 15(1), 1–31 (2007)CrossRefGoogle Scholar
  22. 22.
    Jespersen-Groth, J., et al.: Disruption management in passenger railway transportation. In: Ahuja, R.K., Möhring, R.H., Zaroliagis, C.D. (eds.) Robust and Online Large-Scale Optimization. LNCS, vol. 5868, pp. 399–421. Springer, Heidelberg (2009).  https://doi.org/10.1007/978-3-642-05465-5_18CrossRefGoogle Scholar
  23. 23.
    Graves, S.C.: A review of production scheduling. Oper. Res. 29(4), 646–675 (1981)CrossRefGoogle Scholar
  24. 24.
    Naveh, Y., Richter, Y., Altshuler, Y., Gresh, D.L., Connors, D.P.: Workforce optimization: identification and assignment of professional workers using constraint programming. IBM J. Res. Dev. 51(3.4), 263–279 (2007)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyMonash UniversityClaytonAustralia

Personalised recommendations